This series of files compile all analyses done during Chapter 3:
- Section 1 presents the calculation of the indices
of exposure.
- Section 2 presents variable exploration and
regressions results.
- Section 3 presents species distribution
models.
All analyses have been done with R 4.1.2.
Click on the table of contents in the left margin to assess a
specific analysis.
Click on a figure to zoom it
⏪
| 🏠 | ⏩
1. Spatial variation of exposure indices
Here, we compute semivariograms for each exposure index (on the whole
raster, not only extracted values at the stations).
Aquaculture

Dredging

Runoff

Sewers

Structures

Shipping

Fisheries

2. Relationships with abiotic parameters and biotic indices
Biotic indices have been calculated during Chapter 2.
2.1. Covariation
Several types of models were considered to explore relationships:
linear, quadratic, exponential and logarithmic. The model with the
highest \(R^{2}\) is presented on each
plot.
⚠️ Only linear models are implemented now, as there are some bugs
with the automatized calculation of the others.
Aquaculture


Dredging


Runoff


Sewers


Structures


Shipping


Fisheries


Cumulative exposure


2.2. Correlation
Correlations have been calculated with Spearman’s rank
coefficient.
Correlation coefficients between exposure and
variables/indices
| aquaculture |
-0.088 |
0.065 |
0.023 |
-0.025 |
0.051 |
-0.282 |
-0.24 |
-0.355 |
-0.476 |
-0.483 |
-0.479 |
-0.286 |
-0.267 |
-0.413 |
-0.02 |
-0.055 |
0.158 |
0.152 |
0.209 |
0.223 |
0.191 |
0.157 |
0.262 |
0.204 |
0.18 |
-0.14 |
0.036 |
0.068 |
-0.01 |
-0.192 |
| dredging |
0.238 |
-0.02 |
-0.012 |
0.003 |
0.044 |
0.062 |
0.004 |
0.257 |
0.373 |
0.566 |
0.369 |
-0.025 |
0.126 |
0.28 |
-0.167 |
0.107 |
0.059 |
-0.164 |
-0.05 |
-0.132 |
-0.019 |
0.052 |
-0.027 |
-0.087 |
0.074 |
0.081 |
0.06 |
-0.115 |
-0.015 |
0.155 |
| runoff |
-0.016 |
-0.075 |
0.297 |
-0.194 |
0.018 |
-0.149 |
-0.093 |
0.048 |
0.299 |
0.278 |
0.154 |
-0.126 |
-0.027 |
0.147 |
-0.059 |
-0.068 |
-0.028 |
-0.162 |
-0.065 |
-0.181 |
-0.012 |
0.03 |
-0.085 |
-0.158 |
-0.034 |
0.112 |
0.01 |
0.011 |
-0.004 |
0.031 |
| sewers |
0.424 |
-0.14 |
-0.387 |
0.344 |
0.164 |
0.639 |
0.588 |
0.691 |
0.751 |
0.669 |
0.755 |
0.642 |
0.706 |
0.743 |
-0.091 |
0.053 |
-0.171 |
-0.248 |
-0.23 |
-0.287 |
-0.194 |
-0.108 |
-0.273 |
-0.238 |
-0.187 |
0.176 |
-0.036 |
-0.177 |
0.205 |
0.253 |
| structures |
0.172 |
-0.071 |
0.045 |
-0.006 |
0.076 |
0.052 |
0.044 |
0.277 |
0.465 |
0.514 |
0.404 |
0.057 |
0.165 |
0.326 |
-0.141 |
0.003 |
-0.031 |
-0.219 |
-0.109 |
-0.225 |
-0.054 |
0.012 |
-0.122 |
-0.174 |
-0.035 |
0.047 |
0.039 |
-0.089 |
0.042 |
0.154 |
| shipping |
0.371 |
-0.223 |
-0.228 |
0.238 |
-0.08 |
0.415 |
0.27 |
0.464 |
0.573 |
0.57 |
0.55 |
0.406 |
0.415 |
0.542 |
-0.071 |
0.054 |
-0.003 |
-0.059 |
-0.035 |
-0.059 |
-0.037 |
-0.036 |
-0.082 |
-0.178 |
-0.095 |
0.315 |
-0.045 |
-0.085 |
0.234 |
0.167 |
| fisheries |
-0.492 |
0.202 |
0.376 |
-0.378 |
-0.138 |
-0.567 |
-0.541 |
-0.552 |
-0.606 |
-0.576 |
-0.585 |
-0.54 |
-0.563 |
-0.613 |
0.173 |
-0.066 |
0.097 |
0.309 |
0.224 |
0.28 |
0.167 |
-0.015 |
0.191 |
0.318 |
0.082 |
-0.267 |
0.143 |
0.203 |
-0.226 |
-0.186 |
| cumulative_exposure |
0.277 |
-0.108 |
-0.058 |
0.086 |
0.087 |
0.19 |
0.144 |
0.357 |
0.556 |
0.58 |
0.471 |
0.196 |
0.298 |
0.445 |
-0.092 |
0.03 |
-0.053 |
-0.164 |
-0.107 |
-0.185 |
-0.088 |
-0.048 |
-0.17 |
-0.145 |
-0.075 |
0.215 |
0.033 |
-0.108 |
0.119 |
0.182 |
p-values of correlation test between exposure indices and
variables/indices
| aquaculture |
0.3664 |
0.5051 |
0.8152 |
0.7992 |
0.6021 |
0.003082 |
0.01234 |
0.0001626 |
1.949e-07 |
1.228e-07 |
1.579e-07 |
0.002721 |
0.005256 |
8.895e-06 |
0.8385 |
0.5693 |
0.1014 |
0.1157 |
0.02961 |
0.02045 |
0.04805 |
0.1049 |
0.006131 |
0.03454 |
0.06279 |
0.1478 |
0.7139 |
0.4855 |
0.9216 |
0.04691 |
| dredging |
0.01324 |
0.8387 |
0.9003 |
0.9732 |
0.6495 |
0.5259 |
0.9659 |
0.00727 |
6.998e-05 |
1.771e-10 |
8.557e-05 |
0.799 |
0.1938 |
0.003346 |
0.08431 |
0.269 |
0.5434 |
0.09076 |
0.6071 |
0.1748 |
0.8468 |
0.594 |
0.7845 |
0.3704 |
0.446 |
0.4042 |
0.535 |
0.2368 |
0.8796 |
0.1081 |
| runoff |
0.8701 |
0.4395 |
0.001802 |
0.04443 |
0.8515 |
0.1241 |
0.3393 |
0.623 |
0.001656 |
0.003518 |
0.1125 |
0.1942 |
0.7832 |
0.1285 |
0.5414 |
0.4829 |
0.7721 |
0.09401 |
0.5056 |
0.06025 |
0.9 |
0.7592 |
0.38 |
0.1029 |
0.7294 |
0.2487 |
0.9181 |
0.9107 |
0.9663 |
0.7508 |
| sewers |
4.879e-06 |
0.1485 |
3.54e-05 |
0.0002644 |
0.09067 |
9.873e-14 |
2.137e-11 |
1.233e-16 |
7.406e-21 |
2.565e-15 |
3.667e-21 |
6.987e-14 |
1.478e-17 |
3.514e-20 |
0.3471 |
0.5842 |
0.07716 |
0.00972 |
0.01674 |
0.002579 |
0.04455 |
0.2654 |
0.004234 |
0.01294 |
0.05204 |
0.06905 |
0.7087 |
0.06619 |
0.03313 |
0.008293 |
| structures |
0.07492 |
0.4645 |
0.6446 |
0.9475 |
0.4369 |
0.5895 |
0.6483 |
0.003746 |
3.933e-07 |
1.315e-08 |
1.425e-05 |
0.5586 |
0.08784 |
0.0005685 |
0.1453 |
0.9766 |
0.7536 |
0.02293 |
0.2634 |
0.01934 |
0.576 |
0.8993 |
0.2088 |
0.07181 |
0.7189 |
0.6262 |
0.6861 |
0.3619 |
0.6684 |
0.1117 |
| shipping |
7.89e-05 |
0.02058 |
0.01742 |
0.01331 |
0.4089 |
7.945e-06 |
0.004671 |
4.107e-07 |
9.46e-11 |
1.253e-10 |
7.297e-10 |
1.302e-05 |
7.806e-06 |
1.344e-09 |
0.4653 |
0.5813 |
0.9789 |
0.5475 |
0.7225 |
0.5432 |
0.7071 |
0.7092 |
0.3975 |
0.06563 |
0.3268 |
0.0008844 |
0.6448 |
0.3801 |
0.01464 |
0.08464 |
| fisheries |
6.275e-08 |
0.03585 |
6.17e-05 |
5.585e-05 |
0.1551 |
1.607e-10 |
1.476e-09 |
6.146e-10 |
3.727e-12 |
6.679e-11 |
2.989e-11 |
1.623e-09 |
2.366e-10 |
1.852e-12 |
0.07296 |
0.496 |
0.3185 |
0.001128 |
0.01998 |
0.003357 |
0.0847 |
0.8735 |
0.0474 |
0.0007943 |
0.3984 |
0.005238 |
0.1407 |
0.03473 |
0.01884 |
0.05423 |
| cumulative_exposure |
0.003733 |
0.2652 |
0.5492 |
0.3756 |
0.3707 |
0.04849 |
0.1364 |
0.0001492 |
4.022e-10 |
4.899e-11 |
2.628e-07 |
0.04167 |
0.001703 |
1.356e-06 |
0.3421 |
0.7614 |
0.588 |
0.08962 |
0.2702 |
0.05549 |
0.3636 |
0.6214 |
0.07827 |
0.1342 |
0.4407 |
0.02571 |
0.732 |
0.2679 |
0.2197 |
0.05903 |

3. Relationships with benthic communities
3.1. Species
The most abundant taxa in our study area were:
- Density: B.neotena (1969), E. integra (1158),
P.grandimana (1092), Nematoda (1044) and M. calcarea
(575)
- Biomass: E. parma (biomass of 531.5),
Strongylocentrotus sp. (65.3), N. incisa (58.5),
M. calcarea (45.4) and S. groenlandicus (34.3)
The following graphs present the distribution of sampled phyla along
index of cumulative exposure, according to density (left side) or
biomass (right side).


We analyzed if some phyla or species were characteristic of exposure
classes, and we calculated the IndVal for each class.
Mean density by class
| Annelida |
27.9 |
34.1 |
24.3 |
NA |
NA |
| Arthropoda |
29.9 |
62.7 |
24.4 |
NA |
NA |
| Cnidaria |
0.025 |
0 |
0 |
NA |
NA |
| Echinodermata |
5.55 |
1.92 |
0.389 |
NA |
NA |
| Mollusca |
14.8 |
13.5 |
11.5 |
NA |
NA |
| Nematoda |
20 |
4.9 |
0 |
NA |
NA |
| Nemertea |
0.3 |
0.08 |
0 |
NA |
NA |
| Sipuncula |
0.175 |
0.4 |
0.111 |
NA |
NA |
Total individuals by class
| Annelida |
1116 |
1703 |
437 |
0 |
0 |
| Arthropoda |
1195 |
3136 |
439 |
0 |
0 |
| Cnidaria |
1 |
0 |
0 |
0 |
0 |
| Echinodermata |
222 |
96 |
7 |
0 |
0 |
| Mollusca |
590 |
677 |
207 |
0 |
0 |
| Nematoda |
799 |
245 |
0 |
0 |
0 |
| Nemertea |
12 |
4 |
0 |
0 |
0 |
| Sipuncula |
7 |
20 |
2 |
0 |
0 |
Mean biomass by class
| Annelida |
0.49 |
0.855 |
4.03 |
NA |
NA |
| Arthropoda |
0.158 |
0.114 |
0.0778 |
NA |
NA |
| Cnidaria |
0.0841 |
0 |
0 |
NA |
NA |
| Echinodermata |
11.4 |
1.25 |
4.5 |
NA |
NA |
| Mollusca |
1.11 |
1.56 |
1.75 |
NA |
NA |
| Nematoda |
0.000867 |
0.000286 |
0 |
NA |
NA |
| Nemertea |
5.5e-05 |
0.0342 |
0 |
NA |
NA |
| Sipuncula |
0.0114 |
0.0111 |
0.00468 |
NA |
NA |
Total biomasses by class
| Annelida |
19.6 |
42.7 |
72.6 |
0 |
0 |
| Arthropoda |
6.33 |
5.71 |
1.4 |
0 |
0 |
| Cnidaria |
3.36 |
0 |
0 |
0 |
0 |
| Echinodermata |
454 |
62.4 |
81.1 |
0 |
0 |
| Mollusca |
44.4 |
77.9 |
31.5 |
0 |
0 |
| Nematoda |
0.0347 |
0.0143 |
0 |
0 |
0 |
| Nemertea |
0.0022 |
1.71 |
0 |
0 |
0 |
| Sipuncula |
0.457 |
0.554 |
0.0842 |
0 |
0 |


## cluster indicator_value probability
## harpacticoida 1 0.3173 0.022
## nematoda 2 0.4158 0.002
## ameritella_agilis 2 0.1612 0.017
## nephtyidae_spp 2 0.1571 0.020
## crenella_decussata 2 0.1142 0.042
## byblis_gaimardii 2 0.1000 0.028
##
## Sum of probabilities = 67.135
##
## Sum of Indicator Values = 9.67
##
## Sum of Significant Indicator Values = 1.27
##
## Number of Significant Indicators = 6
##
## Significant Indicator Distribution
##
## 1 2
## 1 5
4. Regressions
For the following analyses, independant variables are
abiotic parameters and exposure indices, dependant variables
are community characteristics. Variables have been standardized by mean
and standard-deviation.
4.1. Data manipulation
All stations and predictors were selected for the regressions, as we
are interested in each of them (following graphs are for information
only).

Correlation coefficients between exposure indices
| aquaculture |
1 |
-0.291 |
-0.536 |
-0.572 |
-0.498 |
-0.485 |
0.456 |
| dredging |
-0.291 |
1 |
0.595 |
0.409 |
0.776 |
0.463 |
-0.28 |
| runoff |
-0.536 |
0.595 |
1 |
0.37 |
0.866 |
0.288 |
-0.302 |
| sewers |
-0.572 |
0.409 |
0.37 |
1 |
0.601 |
0.735 |
-0.686 |
| structures |
-0.498 |
0.776 |
0.866 |
0.601 |
1 |
0.469 |
-0.377 |
| shipping |
-0.485 |
0.463 |
0.288 |
0.735 |
0.469 |
1 |
-0.53 |
| fisheries |
0.456 |
-0.28 |
-0.302 |
-0.686 |
-0.377 |
-0.53 |
1 |

4.2. Univariate regressions
We used linear models for the regressions on community
characteristics. Variables have been standardized by mean and
standard-deviation (coefficients need to be back-transformed to be used
in predictive models). Variable selection was not needed here, as we are
interested in all exposure indices.
Results of regressions (coefficients with a significant p-value for
marginal tests) are shown below. Using both abiotic parameters and
exposure indices as predictors do not increase significantly predictive
power compared to the other models. Details of the regressions for
exposure indices, with diagnostics and cross-validation, are summarized
below.
| Depth |
|
|
+ |
+ |
+ |
|
+ |
|
|
| Aquaculture |
|
|
|
|
|
|
|
|
|
| Dredging |
|
|
|
|
|
|
|
|
+ |
| Runoff |
|
- |
|
+ |
|
|
+ |
|
|
| Sewers |
|
- |
|
|
|
|
|
|
|
| Structures |
|
+ |
|
|
|
|
|
|
|
| Shipping |
|
|
+ |
|
|
|
|
|
|
| Fisheries |
|
|
|
|
|
|
|
|
|
| Adjusted \(R^{2}\) |
0.02 |
0.04 |
0.2 |
0.29 |
0.14 |
0 |
0.1 |
0.04 |
0.13 |
Density
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
1.925e-16 |
0.09539 |
2.018e-15 |
1 |
|
| depth |
-0.185 |
0.1122 |
-1.649 |
0.1024 |
|
| aquaculture |
0.05215 |
0.1347 |
0.3871 |
0.6995 |
|
| dredging |
-0.1216 |
0.129 |
-0.9424 |
0.3483 |
|
| runoff |
0.1879 |
0.2201 |
0.854 |
0.3952 |
|
| sewers |
0.1503 |
0.1855 |
0.8101 |
0.4198 |
|
| structures |
-0.1768 |
0.2539 |
-0.6963 |
0.4879 |
|
| shipping |
-0.09248 |
0.133 |
-0.6955 |
0.4884 |
|
| fisheries |
0.122 |
0.1151 |
1.06 |
0.2918 |
|
## RMSE from cross-validation: 1.06337
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Biomass
## Adjusted R2 is: 0.04
Fitting linear model: B ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-6.466e-17 |
0.09425 |
-6.861e-16 |
1 |
|
| depth |
-0.2062 |
0.1109 |
-1.86 |
0.06585 |
|
| aquaculture |
-0.2534 |
0.1331 |
-1.904 |
0.05983 |
|
| dredging |
-0.009733 |
0.1275 |
-0.07637 |
0.9393 |
|
| runoff |
-0.4785 |
0.2174 |
-2.201 |
0.03008 |
* |
| sewers |
-0.5772 |
0.1833 |
-3.149 |
0.002167 |
* * |
| structures |
0.5394 |
0.2509 |
2.15 |
0.03399 |
* |
| shipping |
0.09399 |
0.1314 |
0.7154 |
0.4761 |
|
| fisheries |
-0.09747 |
0.1138 |
-0.8568 |
0.3936 |
|
## RMSE from cross-validation: 1.018014
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Richness
## Adjusted R2 is: 0.2
Fitting linear model: S ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-1.999e-16 |
0.08613 |
-2.321e-15 |
1 |
|
| depth |
0.2456 |
0.1013 |
2.424 |
0.01716 |
* |
| aquaculture |
0.1377 |
0.1216 |
1.132 |
0.2602 |
|
| dredging |
-0.178 |
0.1165 |
-1.528 |
0.1297 |
|
| runoff |
0.2567 |
0.1987 |
1.292 |
0.1994 |
|
| sewers |
-0.1619 |
0.1675 |
-0.9664 |
0.3362 |
|
| structures |
-0.1495 |
0.2293 |
-0.6521 |
0.5159 |
|
| shipping |
0.2428 |
0.1201 |
2.022 |
0.04587 |
* |
| fisheries |
0.2035 |
0.104 |
1.958 |
0.05306 |
|
## RMSE from cross-validation: 0.9336803
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Diversity
## Adjusted R2 is: 0.29
Fitting linear model: H ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-1.513e-16 |
0.08128 |
-1.861e-15 |
1 |
|
| depth |
0.535 |
0.09561 |
5.596 |
1.956e-07 |
* * * |
| aquaculture |
0.1666 |
0.1148 |
1.451 |
0.1499 |
|
| dredging |
0.005753 |
0.1099 |
0.05234 |
0.9584 |
|
| runoff |
0.4163 |
0.1875 |
2.22 |
0.02868 |
* |
| sewers |
0.002447 |
0.1581 |
0.01548 |
0.9877 |
|
| structures |
-0.3441 |
0.2163 |
-1.59 |
0.115 |
|
| shipping |
0.1112 |
0.1133 |
0.9812 |
0.3289 |
|
| fisheries |
0.02589 |
0.0981 |
0.2639 |
0.7924 |
|
## RMSE from cross-validation: 0.889925
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Evenness
## Adjusted R2 is: 0.14
Fitting linear model: J ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-5.164e-16 |
0.08921 |
-5.789e-15 |
1 |
|
| depth |
0.4573 |
0.1049 |
4.358 |
3.213e-05 |
* * * |
| aquaculture |
0.06496 |
0.126 |
0.5156 |
0.6073 |
|
| dredging |
0.1359 |
0.1206 |
1.126 |
0.2627 |
|
| runoff |
0.3196 |
0.2058 |
1.553 |
0.1236 |
|
| sewers |
0.06674 |
0.1735 |
0.3846 |
0.7013 |
|
| structures |
-0.3271 |
0.2375 |
-1.377 |
0.1715 |
|
| shipping |
-0.04701 |
0.1244 |
-0.3779 |
0.7063 |
|
| fisheries |
-0.1252 |
0.1077 |
-1.163 |
0.2478 |
|
## RMSE from cross-validation: 0.986413
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

AMBI
## Adjusted R2 is: -0.01
Fitting linear model: AMBI ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-2.691e-16 |
0.09647 |
-2.79e-15 |
1 |
|
| depth |
-0.2163 |
0.1135 |
-1.906 |
0.05958 |
|
| aquaculture |
-0.06862 |
0.1362 |
-0.5037 |
0.6156 |
|
| dredging |
0.07402 |
0.1305 |
0.5674 |
0.5717 |
|
| runoff |
-0.2629 |
0.2225 |
-1.181 |
0.2403 |
|
| sewers |
-0.1965 |
0.1876 |
-1.047 |
0.2976 |
|
| structures |
0.3144 |
0.2568 |
1.224 |
0.2237 |
|
| shipping |
-0.05305 |
0.1345 |
-0.3945 |
0.6941 |
|
| fisheries |
0.05485 |
0.1164 |
0.4711 |
0.6386 |
|
## RMSE from cross-validation: 1.04913
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

M-AMBI
## Adjusted R2 is: 0.1
Fitting linear model: M_AMBI ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
2.709e-16 |
0.09154 |
2.959e-15 |
1 |
|
| depth |
0.2857 |
0.1077 |
2.654 |
0.009275 |
* * |
| aquaculture |
0.1601 |
0.1293 |
1.238 |
0.2185 |
|
| dredging |
-0.2233 |
0.1238 |
-1.804 |
0.07433 |
|
| runoff |
0.4915 |
0.2112 |
2.328 |
0.02197 |
* |
| sewers |
0.1389 |
0.178 |
0.7804 |
0.437 |
|
| structures |
-0.3385 |
0.2437 |
-1.389 |
0.1679 |
|
| shipping |
0.009547 |
0.1276 |
0.07482 |
0.9405 |
|
| fisheries |
0.06957 |
0.1105 |
0.6297 |
0.5303 |
|
## RMSE from cross-validation: 1.008127
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

BENTIX
## Adjusted R2 is: 0.04
Fitting linear model: BENTIX ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
2.416e-16 |
0.09441 |
2.559e-15 |
1 |
|
| depth |
0.21 |
0.1111 |
1.891 |
0.0615 |
|
| aquaculture |
0.18 |
0.1333 |
1.35 |
0.1801 |
|
| dredging |
-0.03603 |
0.1277 |
-0.2822 |
0.7784 |
|
| runoff |
0.3955 |
0.2178 |
1.816 |
0.07245 |
|
| sewers |
0.2448 |
0.1836 |
1.333 |
0.1856 |
|
| structures |
-0.3623 |
0.2513 |
-1.442 |
0.1525 |
|
| shipping |
0.1845 |
0.1316 |
1.402 |
0.1641 |
|
| fisheries |
0.008717 |
0.114 |
0.0765 |
0.9392 |
|
## RMSE from cross-validation: 1.040783
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

BOPA
## Adjusted R2 is: 0.13
Fitting linear model: BOPA ~ depth + aquaculture + dredging +
runoff + sewers + structures + shipping + fisheries
| (Intercept) |
6.99e-17 |
0.08982 |
7.783e-16 |
1 |
|
| depth |
0.0981 |
0.1057 |
0.9285 |
0.3554 |
|
| aquaculture |
0.02624 |
0.1268 |
0.2068 |
0.8366 |
|
| dredging |
0.4811 |
0.1215 |
3.961 |
0.0001408 |
* * * |
| runoff |
0.06035 |
0.2072 |
0.2912 |
0.7715 |
|
| sewers |
0.2007 |
0.1747 |
1.149 |
0.2535 |
|
| structures |
-0.2583 |
0.2391 |
-1.08 |
0.2826 |
|
| shipping |
-0.008878 |
0.1252 |
-0.0709 |
0.9436 |
|
| fisheries |
0.01843 |
0.1084 |
0.17 |
0.8654 |
|
## RMSE from cross-validation: 1.068359
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

4.3. Multivariate regressions
Single
Aquaculture
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ aquaculture, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.5405 1.8407 0.034 *
## Residual 106 31.1271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Dredging
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ dredging, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.6326 2.1605 0.012 *
## Residual 106 31.0351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Runoff
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ runoff, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.5265 1.7922 0.026 *
## Residual 106 31.1411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Sewers
## Adjusted R2 is: 0.04
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ sewers, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 1.4864 5.2205 0.001 ***
## Residual 106 30.1812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Structures
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ structures, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.5634 1.9201 0.014 *
## Residual 106 31.1042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Shipping
## Adjusted R2 is: 0.05
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ shipping, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 1.7855 6.3335 0.001 ***
## Residual 106 29.8822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Fisheries
## Adjusted R2 is: 0.02
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ fisheries, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.809 2.7789 0.003 **
## Residual 106 30.859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Cumulative exposure
## Adjusted R2 is: 0.02
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ cumulative_exposure, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.9292 3.2042 0.001 ***
## Residual 106 30.7385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Multiple
Stations are colored according to the cumulative exposure index
(cf. Marine Strategy Framework Directive):
- indigo = lowest exposure (\(E_{ij}\) < 1.4) ~ high status
- green = low exposure (1.4 ≤
\(E_{ij}\) < 2.8) ~ good status
- yellow = moderate exposure (2.8
≤ \(E_{ij}\) < 4.2) ~ moderate
status
- orange = high exposure (4.2 ≤
\(E_{ij}\) < 5.6) ~ poor status
- crimson = highest exposure
(\(E_{ij}\) ≥ 5.6) ~ bad status
Using vegan
The model has a \(R^{2}\) of 0.23
for exposure indices and 0.34 for all variables.


Using PRIMER-e
The model evaluated by the DistLM procedure has a \(R^{2}\) of 0.22 for exposure indices and
0.34 for all variables.


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